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import torch.nn as nn
from torch.nn import TransformerDecoder
from .model import Model
from .transformer import PositionalEncoding, TransformerDecoderLayer
class BCNLanguage(Model):
def __init__(self, dataset_max_length, null_label, num_classes, d_model=512, nhead=8, d_inner=2048, dropout=0.1,
activation='relu', num_layers=4, detach=True, use_self_attn=False, loss_weight=1.0,
global_debug=False):
super().__init__(dataset_max_length, null_label)
self.detach = detach
self.loss_weight = loss_weight
self.proj = nn.Linear(num_classes, d_model, False)
self.token_encoder = PositionalEncoding(d_model, max_len=self.max_length)
self.pos_encoder = PositionalEncoding(d_model, dropout=0, max_len=self.max_length)
decoder_layer = TransformerDecoderLayer(d_model, nhead, d_inner, dropout,
activation, self_attn=use_self_attn, debug=global_debug)
self.model = TransformerDecoder(decoder_layer, num_layers)
self.cls = nn.Linear(d_model, num_classes)
def forward(self, tokens, lengths):
"""
Args:
tokens: (N, T, C) where T is length, N is batch size and C is classes number
lengths: (N,)
"""
if self.detach:
tokens = tokens.detach()
embed = self.proj(tokens) # (N, T, E)
embed = embed.permute(1, 0, 2) # (T, N, E)
embed = self.token_encoder(embed) # (T, N, E)
padding_mask = self._get_padding_mask(lengths, self.max_length)
zeros = embed.new_zeros(*embed.shape)
qeury = self.pos_encoder(zeros)
location_mask = self._get_location_mask(self.max_length, tokens.device)
output = self.model(qeury, embed,
tgt_key_padding_mask=padding_mask,
memory_mask=location_mask,
memory_key_padding_mask=padding_mask) # (T, N, E)
output = output.permute(1, 0, 2) # (N, T, E)
logits = self.cls(output) # (N, T, C)
pt_lengths = self._get_length(logits)
res = {'feature': output, 'logits': logits, 'pt_lengths': pt_lengths,
'loss_weight': self.loss_weight, 'name': 'language'}
return res
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